Supporting Information for the article:
Predicting species distributions for conservation decisions
Antoine Guisan, Reid Tingley, John B. Baumgartner, Ilona Naujokaitis-Lewis, Patricia R.
Sutcliffe, Ayesha I.T. Tulloch, Tracey J. Regan, Lluis Brotons, Eve McDonald-Madden,
Chrystal Mantyka-Pringle, Tara G. Martin, Jonathan R. Rhodes, Ramona Maggini, Samantha
A. Setterfield, Jane Elith, Mark W. Schwartz, Brendan A. Wintle, Olivier Broennimann, Mike
Austin, Simon Ferrier, Michael R. Kearney, Hugh P. Possingham & Yvonne M. Buckley
Ecology Letters
Content
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Title
Species distribution models (SDMs)
Spatial decisions in conservation
Criteria used in the literature search used to build figure 1
Translation of the text and figures of the legal Spanish
decree cited in the main text
Details on the use of SDM results to enforce two decrees to
protect priority conservation areas in Madagascar
Examples of institutions potentially playing the role of
« translator » or « bridge » between science and
management (EnvironmentalEvidences, CONABIO, …).
Other websites, not listed in Table 2, proposing predicted
species distributions (NaturePrint, Map of Life, …)
Additional supporting references
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S1 : Species distribution models (SDMs)
Species distribution models (SDMs) are models that predict environmental suitability for
species in space and time. By relating species occurrences to spatial environmental data,
SDMs fit the realized environmental niche of species. Depending on the perspective taken,
these models are also called ecological niche models (ENMs), habitat suitability models
(HSMs), niche-based models (NBMs), potential habitat distribution models (PHDMs), and
when used only with climate variables, climate-envelope models (CEMs) or climate matching
models (CMMs). A range of approaches can be used to fit them (Guisan & Thuiller 2005;
Elith & Leathwick 2009b; Franklin 2010; Peterson et al. 2011). Projecting these models to
different areas or different time periods allows spatial and temporal extrapolation of species
distributions from a discrete set of observations (Franklin 2010). A wealth of studies have
assessed the theory and assumptions behind these models, their sensitivity to a variety of
factors (e.g. Ferrier et al. 2002; Kadmon et al. 2003; Vaughan & Ormerod 2003; Guisan &
Thuiller 2005; Johnson & Gillingham 2005; Araujo & Guisan 2006; Barry & Elith 2006b;
Elith et al. 2006; Guisan et al. 2007; Jimenez-Valverde & Lobo 2007; Jimenez-Valverde et
al. 2008; Lobo et al. 2008; Elith & Graham 2009; Elith & Leathwick 2009b; JimenezValverde et al. 2009; Buisson et al. 2010; Franklin 2010; Lobo et al. 2010; Sinclair et al.
2010; Barve et al. 2011; McInerny & Purves 2011; Peterson et al. 2011; Araujo & Peterson
2012; Beale & Lennon 2012; Broennimann et al. 2012; Saupe et al. 2012) and the uncertainty
they include (e.g. Elith et al. 2002; Regan et al. 2002; Wintle et al. 2003; Barry & Elith
2006a; Dormann et al. 2008; Buisson et al. 2010; Beale & Lennon 2012). Many studies have
also assessed the appropriateness of SDMs for a range of applications, including climate
change assessment, invasive species, reserve design, and rare or new species discovery (e.g.
Peterson et al. 2002; Raxworthy et al. 2003; Araujo et al. 2004; Brotons et al. 2004; Engler et
al. 2004; Thomas et al. 2004; Thuiller et al. 2005; Wilson et al. 2005; Guisan et al. 2006;
Rhodes et al. 2006; Broennimann & Guisan 2008; Elith & Leathwick 2009a; Sinclair et al.
2010; Araujo et al. 2011; Carvalho et al. 2011; Engler et al. 2011; Thuiller et al. 2011;
Fordham et al. 2012; Schwartz 2012). Depending on the species and environmental data used
to fit them (including the geographic extent they cover and the spatial grain used), the
algorithm used and the way that they are parameterized (e.g. use of model selection or none),
these models can be closer to the actual or potential distribution, depending on the portion of
the realized niche that is captured, whether model overfitting is minimized, and whether
proximal or distal predictors are used. As an expansion of these issues, a debate took place
recently regarding the appropriateness of the main acronyms used to refer to these models:
ecological niche models (ENMs) versus species distribution models (SDMs) (see Warren
2012; McInerny & Etienne 2013; Warren 2013). This debate reflects the uncertainty
associated with the niche concept (Araujo & Guisan 2006; Kearney 2006; McInerny &
Etienne 2012b, a, c). Here, we nevertheless consider these terms to be equivalent, because the
exact same data and algorithms are used in all ENM and SDM studies, but ENM could be
used when the focus wants to be given to niche quantification and SDM when the focus wants
to be given to the spatial predictions. A way to unify these contrasting views would be to
backup more systematically SDM/ENM methodological developments with ecological
understanding (Austin 2002; Austin 2007). Although SDMs are mainly correlative, other
expert or mechanistic modeling approaches also exist. The models and their predictions are
best validated using independent or semi-independent data (e.g. using resampling
approaches). Distribution data can come from various sources (e.g. survey, volunteer
observations, and museum or herbarium records), with variable locational accuracy, and can
be based on abundance, presence-absence or presence-only (occurrence) data, although the
majority of studies use publicly available presence-only data. Accordingly, spatial predictions
can be of abundance, binary presence-absence, or probability of occurrence. Environmental
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layers used for species predictions generally come from various sources (mapping,
interpolations, remote sensing) with variable precision and at different scales, and are best
managed and stored in a Geographic Information System (GIS). Methodological factors that
can affect the predictive power of SDMs and their use in ecological applications include
spatial resolution and multicolinearity of predictors, spatial autocorrelation, precision and
spatial accuracy of data, sample size and bias, and model selection (Franklin 2010). Purely
correlative SDMs provide little information on limiting processes and are prone to
extrapolation errors; however, they may be sufficient to meet many conservation goals, and
are often the only available approach given existing data. Mechanistic models (e.g. based on
ecophysiology or population dynamics) are becoming more common (Kearney & Porter
2009) and can provide useful information for managers (e.g. Florance et al. 2011), but they
are often more data intensive than correlative SDMs. The last decade has seen a real surge in
the scientific development of predictive SDMs and widespread claims of applicability to
conservation problems (Rodriguez et al. 2007; Cayuela et al. 2009; Elith & Leathwick 2009a;
Franklin 2010; Petitpierre et al. 2012).
S2 : Spatial decisions in conservation
Decisions about conservation actions are becoming more spatially explicit. Some aspects of
conservation decision-making, such as protected area design systems, are, by definition,
spatially explicit (Wilson et al. 2009). In the fields of pest control, and fisheries and wildlife
management, most decisions were once aspatial. This is particularly true for fisheries
management where it was convenient to assume that stocks are well mixed at large spatial
scales. However, the increasing availability of fine-scale spatial data, and improved ability for
managers to know exactly where species are located using geographic positioning systems,
has facilitated more spatially explicit decisions and actions (Rhodes et al. 2006).
More recently, the fields of conservation planning, where the implied actions were once only
based on reservation or population management, have started to coalesce, and researchers
have provided approaches for choosing between several spatially-explicit actions (Wilson et
al. 2009; Evans et al. 2011). The increasing application of geographically specific actions
and the timing of these actions demand more spatially-explicit information about their
consequences and purposes. Hence, there is increasing demand for more accurate maps of
species distributions in conservation decision-making. However, the field of species
distribution modeling has traditionally been driven by big questions in biogeography, such as
predicting and explaining the distribution and abundance of organisms (Guisan & Thuiller
2005), and SDMs were rarely constructed with a particular conservation management action
in mind. For example, the needs of basic science are invariably to construct SDMs that
consider the two possible types of errors – presences believed to be absences and vice versa –
equally (i.e. minimizing the sum of false negative and false positives), whereas conservation
practitioners may often wish to have maps biased against a particular kind of SDM error, e.g.
minimizing false absences in assessing which exotic species could potentially invade a
territory.
S3 : Criteria used in the literature search used to build figure 1.
An ISI Web of Science keywords search over the last 20 years (1992-2011) to capture papers
that address species distribution models ("Species distribution model*" OR "habitat model*"
OR "niche model*" OR "habitat distribution model*" OR "habitat suitability model*" OR
"ecological niche model*" OR "niche-based model*" OR "bioclimatic envelope model*" OR
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"resource selection function") returns 2546 papers. When adding keywords for the four
conservation fields addressed in this paper ("invasi*" OR "critical habitat" OR "reserve
selection" OR "reserve design" OR "translocation" OR "assisted colonization"), the number
reduces to 337 (13.2%). Further adding the term “decision” returns just 18 papers (5.3% of
the 337, or only 0.7% of 2546). See trend graphs in Fig. 1.
S4: Translation of the text and figures of the legal Spanish decree cited in the main text,
and describing the ‘critical habitats’ example for 3 critically endangered bird species.
Translated by Lluis Brotons.
“Species distribution models have played a fundamental role in the zoning of expected
impacts and implementation of corrective measures in the context of a large scale irrigation
plan in Eastern Spain threatening bird species protected by the European Bird habitat
directive. For the species present in the area and identified as priority by the bird’s directive
(little bustard, calandra lark (Melanocorypha calandra), short-toed lark (Calandrella
brachydactyla), and the European roller (Coracias garrulus)) SDMs from available data were
developed using Maxent. Corresponding habitat maps were categorised and identified highly
suitable habitats with qualities above the mean habitat quality for the species in the region,
and critical habitats of major potential for the species defined as those highly suitable habitats
with qualities above the mean of the habitat quality of highly suitable areas. The plan agreed
by the Government (http://www.gencat.cat/eadop/imatges/5759/10292099.pdf) used the
different categories of habitat for these species to articulate particular measures ensuring the
persistence of critical sectors of the species in the light of the transformations planned. For
instance, the plan conditions any habitat transformation within critical habitat of major
potential to previous pilot studies demonstrating that such habitat alteration and species
persistence are compatible.”
S5: Details on the use of SDM results to enforce two decrees to protect priority conservation
areas in Madagascar.
In Madagascar, a biodiversity network (« Reseau de la Biodiversite de Madagascar »,
REBIOMA) set up SDMs for large numbers of species in the main biodiversity groups
(mammals, birds, reptiles, amphibians, freswater fishes, invertebrates, plants) with estimated
threat levels to define priority areas for conservation (Kremen et al. 2008; Razafimpahanana
et al. 2008; Allnutt et al. 2009) in the Zonation software (Moilanen et al. 2009). These maps
were then combined with several additional independent analyses of conservation priority,
including Key Biodiversity Areas (Eken et al. 2004), Important Bird Areas (ZICOMA 2001),
Ramsar sites, an unpublished analysis of endemic plant priority areas for endemic plants
produced by the Missouri Botanical Garden, and an unpublished analysis of threatened
vertebrates in the Marxan software (Ball et al. 2009) and put on the map of "potential sites for
conservation". Following a legal decree (Arrêté Interministériel no18633/2008/MEFT/MEM,
and a 2013 extension), no mining and forestry activities can be permitted in these priority
areas for conservation as long as the decree remains in force (see Le Ministre de
l’Environnement des Forêts et du Tourisme & Le Ministre de l’Energie et des Mines 2008;
Vice primature charge du développement et de l'aménagement du territoire & nombreux
autres ministères 2013). More can be read about the whole process in Corson (2011). The
grey literature priority setting report can be accessed at
http://atlas.rebioma.net/index.php?option=com_docman&task=doc_download&gid=29&Itemi
d=29 and the Madagascar Conservation Planning Atlas can be accessed at
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http://www.rebioma.net/index.php?option=com_docman&task=doc_download&gid=8&Itemi
d=17&lang=fr.
S6: Five examples of national and international institutions potentially playing the role of
« translators » (or « conveyors » or « bridge ») between science and management, partially
based on table 1 in Soberón 2004 and descriptions taken from the original websites. See also
the general literature about adaptive governance of social-ecological systems (Cash et al.
2003; Folke et al. 2005; Cumming et al. 2006; Folke 2007). Cumming et al. (2006) is
particularly relevant as it discusses spatial mismatch in governance of social-ecological
systems, with direct implications for SDMs used to support decisions, as these models should
in some cases incorporate some dimensions of the largest scales (e.g. whole climatic niche) to
allow management and containment at continental scales but through coordinated
management at local scales. See also Cash et al. (2003), which refers to knowledge systems
and « the creation of bridges across spatial scales ». The participatory approach advocated in
this paper echos Lubchenco’s (1998) call for a new social contract between researchers and
society in order to address real societal need in a time of enormous human influence on the
planet and its life-support systems. A recent international initiative, the Future Earth Program,
aims to provide sustainability options and solutions by mobilizing scientists and strengthening
partnerships with policy makers and other stakeholders. The actions we propose in this paper
are fully compatible within these large scales, global efforts to bridge the gap between
scientists and environmental decision makers. Examples of institutions with ‘Translator’ role
are:
Two national examples
CONABIO. J. Soberon reported to us : « Mexico provides many very good and published
examples of use of SDMs in actual decision making. In a major planning exercise (convened
by governmental agencies in the Ministry of the Environment), Mexican and foreign scientists
modeled nearly 3,000 species of terrestrial vertebrates to serve as basis for conservation
planning (Koleff et al. 2009a; Urquiza-Haas et al. 2009). All the maps are available on line as
shapefiles via web services in the site of the federal Mexican government biodiversity agency
(CONABIO http://www.conabio.gob.mx/informacion/gis) and many are being used in
government planning. The same agency routinely performs risk-analysis of GMOs
introductions to wildlife relatives in Mexico (more than 3,200 cases since 2000), although the
reports are indeed gray literature published in Spanish. The reports are used by the federal
government to decide whether to grant or not permits for planting GMOs (Soberón et al.
2002), and a recent analyses reporting the predictive value of the GARP modeling used in the
permit process, in the case of transgenic cotton, appears in Wegier et al. (2011). Finally, the
Invading Species unit in CONABIO routinely performs potential distributions analysis that
often are used in the process of making decisions. A good example is the case of the cactus
moth (Cactoblastis cactorum). The analysis performed by CONABIO (Soberón et al. 2001)
was used by the Mexican government to plan monitoring and extermination campaigns and
originally were also referred to by the US Department of Agriculture. A secondary reference
is Simonson et al. (2005). CONABIO uses niche modeling so regularly that they have a unit
of people specialized in these methods (see Soberón et al. 2001; Soberón et al. 2002; Soberón
2004; Koleff et al. 2009b) ». See also the CONABIO webtool in S6 below.
ERIN – Australian Environmental Resources Information Network. On their website at
http://www.environment.gov.au/erin/about.html, it reads : «The Environmental Resources
Information Network (ERIN) is a unit within the Department of Sustainability, Environment,
Water, Population and Communities, specialising in online data and information
management, and spatial data integration and analysis. ERIN aims to improve environmental
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outcomes by developing and managing a comprehensive, accurate and accessible information
base for environmental decisions. Information is drawn from many sources and includes
maps, species distributions, documents and satellite imagery, and covers environmental
themes ranging from endangered species to drought and pollution. Information bases continue
to be established to help answer questions crucial to the conservation and management of our
environment. (…) The answers to these questions are urgently required by government,
industry, researchers and the community. There is also a growing requirement to provide
environmental information for education and regional land management purposes. This will
help determine where conservation effort should be targeted. ».
Three international examples
CEE - Collaboration for Environmental Evidence (Pullin & Knight 2009; Collaboration for
Environmental Evidence 2013; not in Soberon 2004). This is an example of web-based entity
playing a role of translator between scientists and managers. From the website at
http://www.environmentalevidence.org, it reads : «The Collaboration for Environmental
Evidence is an open community of scientists and managers working towards a sustainable
global environment and the conservation of biodiversity. The collaboration seeks to
synthesise evidence on issues of greatest concern to environmental policy and practice. (…)
Its objects are: the protection of the environment and conservation of biodiversity through
preparation, maintenance promotion and dissemination of systematic reviews of the effects
and impacts of environment management interventions, for the public benefit. Syntheses take
the form of systematic reviews providing rigorous and transparent methodology to assess the
impacts of human activity and effectiveness of policy and management interventions. This
website contains a small but fast growing Library of Environmental Evidence in the form of
systematic reviews. The Collaboration is not for profit and relies on the dedication and
enthusiasm of scientists and managers to provide a reliable source of evidence to continuously
improve the effectiveness of our actions ».
UNEP/CBD – Secretariat of the Convention on Biological Diversity (see e.g. Balmford &
Bond 2005). On the CBD website at http://www.cbd.int, it reads: « The Secretariat of the
Convention on Biological Diversity was established to support the goals of the Convention.
Its principal functions are to prepare for, and service, meetings of the Conferences of the
Parties (COP) and other subsidiary bodies of the Convention, and to coordinate with other
relevant international bodies. (…) The Secretariat is institutionally linked to the United
Nations Environment Programme (UNEP), its host institution, and is located in Montreal,
Canada since 1996. (…) The Secretariat assists and provides administrative support to the
COP, SBSTTA and other Convention bodies. It represents the day-to-day focal point for the
Convention, organizes all meetings under the Convention, prepares background
documentation for those meeting and facilitates the flow of authoritative information on the
implementation of the Convention. The Secretariat plays a significant role in coordinating the
work carried out under the Convention with that of other relevant institutions and
conventions, and represents the Convention at meetings of relevant bodies. (…) The
Secretariat plays a significant role in supporting the implementation of the Convention. This
can be fulfilled, for example by compilation of national reports on compliance by domestic
authorities. The Secretariat transmits such reports and information to the COP and sometimes
elaborates a synthesis of the national reports and information on implementation. The
Secretariat also acts as information clearing house. In light of this, the Secretariat is
strengthening its information dissemination activities on public awareness, information and
training, in order to facilitate implementation of Article 13 of the Convention on Public
Education and Awareness».
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FEP - Future Earth Program is an UN-based body promoting interdisciplinary research for
sustainability solutions. From the website, at http://www.icsu.org/future-earth, it reads:
“Future Earth is a new 10-year international research initiative that will develop the
knowledge for responding effectively to the risks and opportunities of global environmental
change and for supporting transformation towards global sustainability in the coming decades.
Future Earth will mobilize thousands of scientists while strengthening partnerships with
policy-makers and other stakeholders to provide sustainability options and solutions in the
wake of Rio+20”.
S7: Other web-tools not included in Table 2, including partially or proposing to include in
the future predicted species distribution maps.
NaturePrint, an SDM-based map of biodiversity values across the state of Victoria
(Australia; NaturePrint 2012), is another comprehensive example of the use of SDMs in a
general conservation-planning framework (i.e. potentially serving multiple objectives). It
integrates information on the spatial distribution and co-location of potentially suitable
habitats for numerous species of mammals, birds, amphibians, reptiles, fish and plants to
assist in the identification of candidate areas for actions with potential consequences for
biodiversity, such as planning for timber harvesting, park management, planned burning,
strategic planning, residential and major infrastructure development and invasive species
management. NaturePrint is available at http://www.dse.vic.gov.au/conservation-andenvironment/biodiversity/natureprint/natureprint-products (last accessed 26.04.2013). The
map of biodiversity values for the State of Victoria (Australia) was produced with the help of
SDMs (Random Forests) for 100 species assemblages representing 3228 plant species and
494 terrestrial animal species, and additional assemblages of 17 freshwater fish species (see
Chee & Elith 2012; Liu et al. 2013). The outputs of these models were summarised in a map
of the assemblage most likely to occur at each pixel. The resulting grids, along with point data
for some rare/threatened species, were used in an optimisation of biodiversity value in
Zonation software (see Moilanen et al. 2009). This was repeated with consideration of
potential for values to be lost/degraded over the next 10 years. This tool was not included in
Table 2 because the maps are static and cannot be calculated in real time, and own data cannot
be uploaded. SDMs were also used in Victoria for use in regulating vegetation clearing
applications (DEPI 2013).
Map-of-Life is available at http://www.mappinglife.org (last accessed 26.04.2013). At the
time of this paper publication, it was still only available as a demo release. In its current state,
Map of Life can «map and produce list of species anywhere for ~ 46,000 species », but the
possibility to fit species distribution models and map the resulting prediction is only
advertised as a future goal. This may nevertheless represent a major tool of tomorrow’s webbased products for life mapping associated with large biological occurrence databases,
including habitat suitability mapping as a tool for data integration (see Jetz et al. 2012).
CONABIO server is available at http://www.conabio.gob.mx/informacion/gis (last accessed
16.05.2013). J. Soberon reported to us : « In a major planning exercise (convened by
governmental agencies in the Ministry of the Environment), Mexican and foreign scientists
modeled nearly 3,000 species of terrestrial vertebrates to serve as basis for conservation
planning (Koleff et al., 2009, Urquiza-Haas et al., 2009). All the maps are available on line as
shapefiles via web services in the site of the federal Mexican government biodiversity agency
(CONABIO) and many are being used in government planning. ». See text for CONABIO in
S6 above.
7
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